|Oracle® R Enterprise User's Guide
|PDF · Mobi · ePub|
Releases of Oracle R Enterprise often contain new features. The features in the current release and in some previous releases are described in the following topics:
The following topics describe the changes in Oracle R Enterprise 1.4:
The following changes are in Oracle R Enterprise 1.4:
Additions and improvements to data preparation functions:
factanal function performs factor analysis on a
formula or an
ore.frame object that contains numeric columns.
Both signatures of the
princomp function support the
getXlevels function creates a list of factor levels that can be used in the
xlev argument of a
model.matrix call that involves an
The new exploratory data analysis function
ore.esm builds exponential smoothing models for time series data. The function builds a model using either the simple exponential smoothing method or the double exponential smoothing method. The function can preprocess the time series data with operations such as aggregation and the handling of missing values. See "Building Exponential Smoothing Models on Time Series Data".
Additions and improvements to the Oracle R Enterprise regression and neural network modeling functions:
ore.glm function provides methods for fitting generalized linear models, which include logistic regression, probit regression, and poisson regression. See "Building a Generalized Linear Model".
ore.stepwise functions are no longer limited to a total of 1,000 columns when deriving columns in the model formula.
ore.lm function now supports a
weights argument for performing weighted least squares regression.
anova function can now perform analysis of variance on an
ore.stepwise function, the values for the
direction argument have changed. The value
"both" now prefers drops over adds. The new
direction argument value
"alternate" has the previous meaning of the
ore.neural function has several new arguments.
Additions and improvements to the Oracle Data Mining model algorithm functions:
ore.odmAssocRules function, which builds an Oracle Data Mining association model using the apriori algorithm. See "Building an Association Rules Model".
ore.odmNMF function, which builds an Oracle Data Mining model for feature extraction using the Non-Negative Matrix Factorization (NMF) algorithm. See "Building a Non-Negative Matrix Factorization Model".
ore.odmOC function, which builds an Oracle Data Mining model for clustering using the Orthogonal Partitioning Cluster (O-Cluster) algorithm. See "Building an Orthogonal Partitioning Cluster Model".
An additional global option for Oracle R Enterprise,
ore.parallel. See "Oracle R Enterprise Global Options".
The following topics describe the changes in Oracle R Enterprise 1.3:
The new features in Oracle R Enterprise 1.3 are the following:
Predicting with R models using in-database data with the
Ordering and indexing with
Predicting with Oracle Data Mining models using the
Saving and managing R objects in the database
Date and time data types
Sampling and partitioning
Long names for columns
Automatically connecting to an Oracle Database instance in embedded R scripts
Building an R neural network using in-database data with the
Other changes in this release are the following:
Installation and administration information has moved from this manual to Oracle R Enterprise Installation and Administration Guide. New features related to installation and administration are described in that book.
The new features in Oracle R Enterprise 1.1 are the following:
Support for additional operation systems:
Oracle R Distribution and Oracle R Enterprise are now supported IBM AIX 5.3 and higher and on 10 and higher for both 64-bit SPARC and 64-bit x386 (Intel) processors.
The Oracle R Enterprise Server now runs on 64-bit and 32-bit Windows operating systems.
Improved mathematics libraries in R:
You can now use the improved Oracle R Distribution with support for dynamically picking up either the Intel Math Kernel Library (MKL) or the AMD Core Math Library (ACML) with Oracle R Enterprise.
On Solaris, Oracle R Distribution dynamically links with Oracle SUN performance library for high speed BLAS and LAPACK operations.
Support for Oracle Wallet enables R scripts to no longer need to have database authentication credentials in clear text. Oracle R Enterprise is integrated with Oracle Wallet for that purpose.
Improved installation scripts provide more prerequisite checks and detailed error messages. Error messages provide specific instructions on remedial actions.